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Co-authored-by: Bowen Yao <112051015+BowenYao18@users.noreply.github.com> Co-authored-by: BowenYao18 <by18@rice.edu>
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@@ -58,12 +58,12 @@ def create_dataloader(
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datapipe = datapipe.copy_to(device=device)
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need_copy = False
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node_feature_keys = {"paper": ["feat"], "author": ["feat"]}
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if args.dataset == "ogb-lsc-mag240m":
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node_feature_keys = {
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"paper": ["feat"],
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"author": ["feat"],
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"institution": ["feat"],
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}
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node_feature_keys["institution"] = ["feat"]
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if "igb-het" in args.dataset:
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node_feature_keys["institute"] = ["feat"]
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node_feature_keys["fos"] = ["feat"]
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# Fetch node features for the sampled subgraph.
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datapipe = datapipe.fetch_feature(features, node_feature_keys)
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@@ -335,8 +335,13 @@ def parse_args():
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"--dataset",
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type=str,
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default="ogb-lsc-mag240m",
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choices=["ogb-lsc-mag240m"],
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help="Dataset name. Possible values: ogb-lsc-mag240m",
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choices=[
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"ogb-lsc-mag240m",
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"igb-het-tiny",
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"igb-het-small",
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"igb-het-medium",
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],
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help="Dataset name. Possible values: ogb-lsc-mag240m, igb-het-[tiny|small|medium].",
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)
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parser.add_argument(
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"--fanout",
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@@ -990,6 +990,16 @@ class BuiltinDataset(OnDiskDataset):
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Self edges are added to the original graph.
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Node features are stored as float32.
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**igb-het-[tiny|small|medium]**
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The igb-hom-[tiny|small|medium] dataset is a heterogeneous citation network,
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which is designed for developers to train and evaluate GNN models with
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high fidelity. See more details in `igb-het-[tiny|small|medium]
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<https://github.com/IllinoisGraphBenchmark/IGB-Datasets>`_.
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.. note::
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Four Reverse edge types are added to the original graph.
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Node features are stored as float32.
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Parameters
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----------
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name : str
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@@ -1019,6 +1029,10 @@ class BuiltinDataset(OnDiskDataset):
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"igb-hom-tiny-seeds",
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"igb-hom-small",
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"igb-hom-small-seeds",
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"igb-het-tiny",
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"igb-het-tiny-seeds",
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"igb-het-small",
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"igb-het-small-seeds",
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]
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_large_datasets = [
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"ogb-lsc-mag240m",
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@@ -1031,6 +1045,8 @@ class BuiltinDataset(OnDiskDataset):
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"igb-hom-large-seeds",
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"igb-hom",
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"igb-hom-seeds",
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"igb-het-medium",
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"igb-het-medium-seeds",
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]
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_all_datasets = _datasets + _large_datasets
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